In the cloud industry, a user often purchases a desired resource specification, including a storage space, a virtual machine, a computing capability, and the like, according to his/her own requirements and the requirements of a business scenario. At present, in a cloud industry market, a specification of a cloud product resource is fixed in purchase. Later, the user increases or reduces the resource specification according to his/her own needs, and a back-end technician updates a resource specification of a cloud product service thereof according to the capacity expansion or reduction of demand submitted by the user.
Usually, cloud product service resources used by the user do not exceed the purchased resource specification; service resources can be fully used or, during special periods, even an upper limit of the specification may be exceeded. In addition, most users do not know their own service use conditions, and may put forward a capacity expansion or reduction demand only when the service resources are used up or services have not been in use for a long time.
In the conventional techniques, an online dynamic capacity expansion method is used, in which, through real-time acquisition of a system load state and an application running condition, an artificial neural network is used as a mechanism that determines resource expansion and reduction trigger, and virtual computing resources are dynamically expanded without interrupting a service. However, the solution focuses on a real-time load condition of the resource per se, and the resource specification is expanded in real time in the peak of resource use, which lacks predictability.